Legal claims defining the scope of protection. Each claim is shown in both the original legal language and a plain English translation.
1. A computing system comprising: a processor; and computer storage memory having computer-executable instructions stored thereon which, when executed by the processor, configure the computing system to facilitate data transformations, the computing system configured to: receive a set of example values including one or more example input values that indicate data values to be transformed and one or more example output values that indicate a desired form in which to transform data; based on at least a portion of the set of example values, identifying a data transformation function that is relevant to the set of example values; generating a transformation program that uses the data transformation function and a supplemental transformation tool to transform the one or more example input values to the desired form in which to transform data, wherein the supplemental transformation tool is utilized based on a determination that the data transformation function does not result in the desired form in which to transform data, and wherein the supplemental transformation tool is applied to an output value of the data transformation function; and providing a suggestion of the transformation program, wherein selection of the transformation program suggestion results in a data transformation.
This invention relates to automated data transformation in computing systems, addressing the challenge of efficiently converting input data into a desired output format without manual programming. The system receives example input and output values that demonstrate the desired transformation. Using these examples, it identifies a relevant data transformation function, such as mathematical operations or string manipulations, that can partially or fully achieve the transformation. If the function alone does not produce the exact desired output, the system supplements it with additional transformation tools, such as filtering, sorting, or formatting utilities, applied to the intermediate results. The system then generates a transformation program combining these functions and tools, providing a suggested solution that, when selected, automatically applies the transformation to new data. This approach reduces the need for manual coding by leveraging example-based learning and modular transformation tools to handle complex data conversion tasks. The system is particularly useful in data processing workflows where users may lack programming expertise but need to adapt data formats for analysis or integration.
2. The computing system of claim 1 , wherein the set of example values is received from a user device based on a user selection of the set of example values.
A computing system is designed to process and analyze data sets, particularly for tasks such as machine learning, data modeling, or statistical analysis. The system addresses the challenge of efficiently handling user-provided data inputs, ensuring accurate and relevant processing based on user preferences. The system includes a data processing module that receives and processes a set of example values, which are numerical or categorical data points used to train or evaluate models. These example values are obtained from a user device, where a user actively selects the specific set of values to be processed. The selection process allows users to customize the data inputs, ensuring the system processes only the most relevant or desired data points. The system may also include a user interface module that facilitates the selection and submission of these example values, providing a seamless interaction between the user and the computing system. By enabling user-driven data selection, the system enhances flexibility and precision in data analysis, ensuring the results align with the user's specific needs or objectives. The overall architecture ensures efficient data handling, reducing processing time and improving accuracy in model training or analysis tasks.
3. The computing system of claim 1 , wherein the data transformation function is identified as relevant to the set of example values based on a comparison of at least one of the one or more example input values to one or more calling input examples associated with the data transformation function.
A computing system processes data by applying a data transformation function to input values. The system identifies relevant transformation functions by comparing example input values to calling input examples associated with each function. This comparison determines whether a function is applicable to the given input data. The system may also generate a data transformation graph that represents the sequence of transformations applied to the input values, where nodes in the graph correspond to data transformation functions and edges represent the flow of data between functions. The graph can be used to visualize or analyze the transformation process. The system further includes a user interface that allows users to interact with the data transformation graph, such as by selecting nodes to view details about the corresponding functions or modifying the graph structure. The system may also support collaborative editing, where multiple users can simultaneously edit the graph, with changes being synchronized in real time. The overall goal is to improve data processing efficiency and transparency by dynamically identifying and applying relevant transformation functions based on input data characteristics.
4. The computing system of claim 3 , wherein the at least one of the one or more example input values matches at least one of the one or more calling input examples.
A computing system is designed to process input values by comparing them against stored input examples to determine matches. The system includes a memory storing one or more calling input examples and a processor configured to receive one or more example input values. The processor evaluates whether at least one of the received example input values matches at least one of the stored calling input examples. This comparison process enables the system to identify when input values align with predefined examples, facilitating tasks such as input validation, pattern recognition, or decision-making based on the presence of matching values. The system may be used in applications requiring input verification, such as authentication systems, data processing pipelines, or machine learning models where input consistency is critical. The matching process ensures that the system can reliably detect and respond to specific input patterns, improving accuracy and reducing errors in automated workflows.
5. The computing system of claim 3 , wherein the at least one of the one or more example input values corresponds with a calling pattern associated with at least one of the one or more calling input examples.
The invention relates to computing systems designed to analyze and process input values, particularly in the context of recognizing and responding to specific calling patterns. The system includes a processor and memory storing instructions that, when executed, enable the system to receive one or more input values and compare them against predefined calling input examples. These examples represent known patterns or sequences of inputs that the system is configured to detect. The system evaluates whether at least one of the received input values matches or corresponds with a calling pattern associated with the stored examples. If a match is found, the system may trigger a predefined action or response, such as executing a function, generating an output, or modifying its behavior. The system may also include additional components, such as a user interface for input and output, or communication interfaces for transmitting data to other devices. The primary problem addressed by this invention is the need for computing systems to efficiently and accurately recognize and respond to specific input patterns, which is particularly useful in applications like voice recognition, command processing, or automated workflows where precise pattern matching is required. The system ensures that only relevant or authorized inputs are processed, improving security and efficiency in automated systems.
6. The computing system of claim 1 further comprising collecting the data transformation function based on a web search or enterprise search in which the data transformation function is identified as a candidate transformation function.
This invention relates to computing systems that process and transform data, particularly in scenarios where data transformation functions are dynamically identified and applied. The problem addressed is the challenge of efficiently locating and applying relevant data transformation functions in large-scale data processing environments, such as those involving web or enterprise search systems. Traditional approaches often rely on predefined or manually selected transformations, which may not adapt well to evolving data requirements or diverse data sources. The system includes a mechanism for collecting data transformation functions by performing a web or enterprise search to identify candidate transformation functions. These functions are then evaluated and selected based on their relevance to the data processing task at hand. The system may also include components for executing the selected transformations, monitoring their performance, and refining the search criteria to improve future function selection. This approach enables dynamic adaptation to changing data formats, sources, or processing needs, enhancing flexibility and efficiency in data workflows. The invention is particularly useful in environments where data transformations must be applied across heterogeneous datasets or where transformation requirements are not known in advance.
7. The computing system of claim 1 , wherein the supplemental tool comprises a second transformation function, a transformation table, a transformation service, or a transformation operator.
This invention relates to computing systems designed to process and transform data. The core system includes a primary data transformation mechanism that processes input data according to a defined transformation function. The challenge addressed is the need for flexibility in data transformation, allowing for additional processing steps or alternative methods beyond the primary function. The supplemental tool enhances the primary transformation by providing secondary transformation capabilities. This tool can take the form of a second transformation function, which applies an additional set of rules or operations to the data. Alternatively, it may use a transformation table, which maps input values to output values for quick reference. Another option is a transformation service, which could be a separate module or API that performs specialized transformations. Lastly, the tool may include a transformation operator, a programmable element that executes specific transformation logic. The supplemental tool operates in conjunction with the primary transformation, allowing for more complex or customized data processing. This modular approach enables users to adapt the system to different data formats, requirements, or workflows without altering the core transformation logic. The invention improves efficiency and versatility in data handling, particularly in systems where multiple transformation steps or alternative methods are needed.
8. The computing system of claim 1 , wherein the supplemental transformation tool performs a syntactic transformation.
A computing system is designed to process and transform data, particularly focusing on improving data usability and compatibility. The system includes a primary transformation tool that converts input data into a standardized format, addressing issues such as inconsistencies, missing values, or incompatible structures. To enhance this process, the system incorporates a supplemental transformation tool that applies additional transformations to the data. Specifically, this supplemental tool performs a syntactic transformation, which involves modifying the structure or syntax of the data without altering its semantic meaning. For example, it may reformat text, adjust data types, or reorganize fields to ensure compatibility with downstream applications. The system may also include a validation module to verify the accuracy and integrity of the transformed data, ensuring it meets predefined criteria before further processing or analysis. This approach improves data quality, reduces errors, and enhances interoperability across different systems and applications. The syntactic transformation ensures that data remains consistent and usable while adapting to various formats and requirements.
9. The computing system of claim 1 , wherein the supplemental transformation tool is identified by: executing the data transformation function using an example input value to obtain an output value from the data transformation function; based on a comparison of the output value resulting from execution of the data transformation to one of the example output values, identifying the supplemental transformation tool to use with the data transformation function that obtains the desired form in which to transform data.
This invention relates to computing systems that transform data using supplemental tools to achieve a desired output format. The problem addressed is the difficulty in automatically selecting the appropriate supplemental transformation tool when multiple tools could potentially modify the output of a data transformation function to meet specific formatting requirements. The system includes a data transformation function that processes input data and a supplemental transformation tool that further modifies the output to achieve the desired format. To identify the correct supplemental tool, the system executes the data transformation function with an example input value, producing an output. This output is then compared against predefined example output values that represent the desired format. Based on this comparison, the system selects the supplemental transformation tool that, when applied to the transformation function's output, produces the closest match to the desired format. This ensures that the data is transformed into the correct structure or representation required by downstream processes or applications. The approach automates the selection of transformation tools, reducing manual intervention and improving efficiency in data processing workflows.
10. A computer-implemented method for facilitating data transformations, the method comprising: receiving a set of example values including one or more example input values that indicate data values to be transformed and one or more example output values that indicate a desired form in which to transform data; based on at least a portion of the set of example values, identifying a data transformation tool that is relevant to the set of example values; generating a transformation program that uses the data transformation tool and a supplemental transformation tool to transform the one or more example input values to the desired form in which to transform data, wherein the supplemental transformation tool is utilized based on a determination that the data transformation function does not result in the desired form in which to transform data, and wherein the supplemental transformation tool is applied to an output value of the data transformation function; and providing one or more transformed output values resulting from execution of the transformation program using at least a portion of the one or more example input values.
This invention relates to automated data transformation in computing systems, addressing the challenge of efficiently converting input data into a desired output format without manual programming. The method receives example input and output values that define the transformation requirements. Using these examples, it identifies a primary data transformation tool capable of handling the task. If the primary tool alone cannot achieve the desired output, the system supplements it with an additional transformation tool, applying the secondary tool to the intermediate results produced by the primary tool. The combined transformation process is then executed to generate the final output values. This approach automates the creation of transformation programs by leveraging example-based learning and tool chaining, reducing the need for manual coding and improving efficiency in data processing workflows. The system dynamically selects and combines tools to ensure accurate transformations, even when a single tool is insufficient. This method is particularly useful in data integration, ETL (Extract, Transform, Load) processes, and automated data pipeline construction.
11. The method of claim 10 further comprising searching for the data transformation tool from a collection of data transformation tools.
A system and method for identifying and applying data transformation tools to process data involves searching for an appropriate tool from a collection of available tools. The method begins by analyzing input data to determine its structure, format, and content. Based on this analysis, the system identifies a target data structure or format that the input data should be converted into. The system then searches through a collection of data transformation tools to find one that can perform the required conversion. Each tool in the collection is capable of transforming data between different formats, structures, or representations. The selected tool is then applied to the input data to generate the transformed output. The system may also validate the transformed data to ensure it meets the target specifications. This approach automates the selection and application of data transformation tools, reducing manual effort and improving efficiency in data processing workflows. The method is particularly useful in environments where data must be frequently converted between different formats, such as in data integration, migration, or analytics pipelines.
12. The method of claim 11 , wherein each of the data transformation tools comprises transformation functions.
A system and method for data processing involves a platform that integrates multiple data transformation tools to process data from various sources. The system addresses the challenge of efficiently handling diverse data formats and structures by providing a unified framework for applying transformation functions. Each data transformation tool includes specific transformation functions designed to modify, clean, or restructure data according to predefined rules or user-defined specifications. These functions may include operations such as filtering, aggregation, normalization, or data type conversion. The tools are configured to process data in a sequential or parallel manner, depending on the requirements of the data workflow. The system ensures compatibility with different data sources by standardizing input and output formats, allowing seamless integration into existing data pipelines. The transformation functions are modular and can be updated or customized without disrupting the overall workflow. This approach enhances data quality, consistency, and usability across various applications, such as analytics, reporting, or machine learning. The system also supports real-time or batch processing, depending on the deployment configuration. By automating data transformation tasks, the system reduces manual effort and minimizes errors, improving overall efficiency in data management.
13. The method of claim 10 , wherein the data transformation tool is identified as relevant to the set of example values based on a syntactic distance between at least one of the example values and a calling example pattern associated with the data transformation tool.
This invention relates to identifying relevant data transformation tools based on syntactic similarity. The problem addressed is efficiently matching transformation tools to specific data values in a system where tools are selected based on input patterns. The method involves comparing example values from a dataset to predefined calling example patterns associated with available transformation tools. A syntactic distance metric measures the similarity between the example values and the calling patterns. Tools with the closest syntactic match are identified as relevant. The syntactic distance may be calculated using techniques such as string similarity, token matching, or pattern recognition. The method ensures that the most appropriate transformation tool is selected for processing the example values, improving accuracy and efficiency in data transformation workflows. The approach is particularly useful in automated data processing systems where manual tool selection is impractical. The invention enhances the adaptability of transformation pipelines by dynamically matching tools to input data characteristics.
14. The method of claim 10 further comprising providing a suggestion of the transformation program to a user device.
A system and method for software transformation involves analyzing a source codebase to identify transformation opportunities, such as refactoring or optimization, and generating a transformation program to apply these changes. The system evaluates the impact of potential transformations on the codebase, including performance, readability, and maintainability, to determine the most beneficial modifications. It then generates a transformation program that implements these changes while preserving the original functionality of the code. The transformation program is executed to modify the source codebase according to the identified opportunities. Additionally, the system provides suggestions for the transformation program to a user device, allowing users to review and approve or modify the proposed changes before execution. This approach automates the process of code improvement while ensuring transparency and user control over the transformations applied. The system is particularly useful in large-scale software development environments where manual refactoring is time-consuming and error-prone.
15. The method of claim 14 , wherein the one or more transformed output values are provided based on a selection of the suggested transformation program.
This invention relates to data transformation systems, specifically methods for selecting and applying transformation programs to input data. The problem addressed is the need for efficient and accurate transformation of data values in a computing environment, particularly when multiple transformation options are available. The method involves generating one or more transformed output values from input data by applying a transformation program. The transformation program is selected from a set of suggested transformation programs, which are generated based on an analysis of the input data. The selection process considers the suitability of each suggested program for the specific input data, ensuring the most appropriate transformation is applied. The transformed output values are then provided based on the selected transformation program, allowing for accurate and efficient data processing. The transformation programs may include various operations such as data normalization, filtering, aggregation, or other data manipulation techniques. The selection process may involve evaluating the suggested programs against predefined criteria, such as performance metrics, accuracy, or compatibility with the input data. The method ensures that the transformed output values are generated in a manner that maintains data integrity and meets the requirements of the data processing task. This approach improves the efficiency and reliability of data transformation in computing systems.
16. One or more computer storage media having computer-executable instructions embodied thereon that, when executed by one or more processors, cause the one or more processors to perform a method for facilitating data transformations, the method comprising: receiving a set of example values including one or more example input values that indicate data values to be transformed and one or more example output values that indicate a desired form in which to transform data; analyzing the set of example values to identify a data transformation function that is relevant to the transformation of the set of example values; executing the data transformation function using an example input value to obtain an output value from the data transformation function; based on determining by a comparison of the output value resulting from execution of the data transformation function to one of the example output values that the data transformation function does not result in the desired data transformation, identifying a transformation tool to apply to the output value from the data transformation function that obtains the desired form in which to transform data; and generating a transformation program using the data transformation function and the transformation tool, the transformation program being usable to transform the at least a portion of the one or more example input values to the desired form in which to transform data.
The invention relates to automated data transformation systems that generate transformation programs based on example input and output values. The problem addressed is the manual effort required to define data transformation rules, particularly when dealing with complex or non-standard data formats. The system receives a set of example values, including input data and corresponding desired output formats. It analyzes these examples to identify an initial data transformation function that could apply to the input values. The system then executes this function on an example input to produce an output. If this output does not match the desired format, the system identifies an additional transformation tool that can refine the output to achieve the correct form. The system combines the initial function and the refinement tool into a transformation program, which can then be applied to other input values to produce the desired output format. This approach automates the creation of transformation logic, reducing the need for manual rule definition and improving efficiency in data processing workflows. The system is particularly useful in scenarios where transformation rules are not predefined or when dealing with diverse data formats.
17. The media of claim 16 further comprising providing a suggestion of the transformation program, wherein selection of the transformation program results in a data transformation.
This invention relates to data transformation systems, specifically improving the efficiency and usability of selecting and applying transformation programs to data. The problem addressed is the complexity and time required for users to identify and apply appropriate transformation programs to modify data in a desired way, particularly in environments where multiple transformation options exist. The system includes a user interface that displays available transformation programs and provides suggestions for selecting a transformation program based on user input or context. When a user selects a suggested transformation program, the system automatically applies the transformation to the data, modifying it according to the program's defined rules. The suggestion mechanism may consider factors such as the type of data, the user's previous selections, or predefined transformation workflows to recommend the most relevant options. This reduces the need for manual searching or trial-and-error selection, streamlining the data transformation process. The system may also include features for managing and organizing transformation programs, such as categorizing them by function or data type, to further enhance usability. The overall goal is to simplify the process of applying data transformations, making it faster and more intuitive for users.
18. The media of claim 16 further comprising providing one or more transformed output values generated from execution of the transformation program.
This invention relates to data transformation systems, specifically methods for processing and transforming data values using a transformation program. The system addresses the challenge of efficiently applying complex transformations to input data while ensuring accurate and reliable output. The transformation program is designed to process input values and generate transformed output values based on predefined rules or algorithms. These transformed outputs can then be used for further analysis, reporting, or integration into other systems. The system may also include mechanisms for validating the transformation process, ensuring that the output values meet specified criteria or constraints. Additionally, the transformation program may be configured to handle different types of input data, such as numerical, textual, or categorical values, and apply appropriate transformations to each type. The system may also support batch processing, allowing multiple input values to be transformed simultaneously for improved efficiency. The transformed output values can be stored, displayed, or transmitted to other systems for further use. This invention aims to provide a flexible and scalable solution for data transformation, enabling users to process and analyze data more effectively.
19. The media of claim 16 , wherein the transformation tool comprises a second data transformation function, a transformation table, or a transformation service.
This invention relates to data transformation systems, specifically addressing the challenge of efficiently processing and converting data between different formats or structures. The system includes a transformation tool designed to facilitate data transformation operations. The transformation tool may incorporate a second data transformation function, which is a programmable or configurable function that processes input data according to predefined rules or algorithms. Alternatively, the transformation tool may use a transformation table, which is a structured dataset that maps input values to corresponding output values, enabling quick lookups and conversions. Another option is a transformation service, which is a network-accessible or cloud-based service that performs data transformation tasks on behalf of the system. The transformation tool is integrated into a broader data processing pipeline, where it receives input data, applies the selected transformation method, and outputs the transformed data for further use. This approach enhances flexibility and scalability in data processing workflows by providing multiple transformation mechanisms tailored to different use cases. The system ensures compatibility with various data formats and supports dynamic adaptation to changing transformation requirements.
20. The media of claim 16 , wherein the transformation tool comprises a transformation operator that performs a syntactic transformation comprising one or more of a concatenation operation, a split operation, and a numeric operation.
The invention relates to a system for transforming data within a computing environment, specifically addressing the need for efficient and flexible data manipulation. The system includes a transformation tool that processes input data to generate output data, where the transformation is defined by a transformation operator. This operator performs syntactic transformations on the input data, which may include concatenation operations to combine data elements, split operations to divide data elements, and numeric operations to perform arithmetic or mathematical functions on numeric data. The transformation tool applies these operations based on predefined rules or user-defined configurations, allowing for customizable data processing workflows. The system is designed to handle various data types and formats, ensuring compatibility with different data sources and applications. The transformation tool may be integrated into larger data processing pipelines or used as a standalone utility for data preparation and manipulation tasks. The invention aims to provide a robust and versatile solution for transforming data in real-time or batch processing scenarios, improving data consistency and usability across different systems and applications.
Unknown
April 14, 2020
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